Dynamic channel selection algorithms for interference management in Wi-Fi networks

ABSTRACT

A method of dynamically adjusting Wi-Fi parameters of a plurality of access points (APs) is disclosed. Reports from the plurality of APs are received through an interface. A conflict graph is created by a processor. Creating the conflict graph includes creating a plurality of vertices of the conflict graph, each vertex corresponding to one of the plurality of APs. Creating the conflict graph further includes determining that there is a conflict between at least some of the pairs of APs based at least in part on the received reports. Creating the conflict graph includes connecting an edge between each of the at least some of the pairs of APs. The conflict graph is stored in a memory.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 13/958,853, entitled DYNAMIC CHANNEL SELECTION ALGORITHMS FORINTERFERENCE MANAGEMENT IN WIFI NETWORKS, filed Aug. 5, 2013; whichclaims priority to U.S. Provisional Patent Application No. 61/680,169,entitled DYNAMIC CHANNEL SELECTION ALGORITHMS FOR INTERFERENCEMANAGEMENT IN WIFI NETWORKS, filed Aug. 6, 2012; U.S. Provisional PatentApplication No. 61/698,426, entitled DYNAMIC CHANNEL SELECTION IN WI-FINETWORKS, filed Sep. 7, 2012; and U.S. Provisional Patent ApplicationNo. 61/705,076, entitled POWER CONTROL AND CARRIER-SENSE THRESHOLDOPTIMIZATION IN SON-FOR-WIFI, filed Sep. 24, 2012, all of which areincorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

Wi-Fi networks are increasingly used for high-speed wirelessconnectivity in the home, office, and in public hotspots. Wi-Fi networksare designed based on the family of IEEE 802.11 Wi-Fi standards,including the early 802.11a/b/g standards, the current 802.11n standard,and the next-generation 802.11 ac standard, which provides for peak datarates exceeding 1 Gigabits per second (Gbps). These standards have beendesigned to target access points (APs) with relatively small coverageareas and low-density deployments. In dense deployments, theinterference between the APs can be severe, and the Wi-Fi standards lacksophisticated mechanisms to mitigate the interference. As a consequence,in dense deployments current Wi-Fi systems may exhibit poor spectrumreuse and significant contention among APs and their associated clients,resulting in low throughput and poor end-user experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 illustrates a Wi-Fi network, in which a Wi-Fi-enabled client,client 101, client 102, or client 103, may connect to any authorized AP(104 or 106) when the client is within the AP's coverage area.

FIG. 2A illustrates that when the carrier sense threshold (CST) is high,the carrier sense radius is small.

FIG. 2B illustrates that when the CST is low, the carrier sense radiusis large.

FIG. 3 illustrates an embodiment of a Wi-Fi-network manager forautomatically and dynamically configuring and updating Wi-Fi APparameters in one or more Wi-Fi networks to optimize the overall networkperformance of the one or more Wi-Fi networks.

FIG. 4 is a flow chart illustrating an embodiment of a process 400 forautomatically and dynamically configuring and updating Wi-Fi APparameters in one or more Wi-Fi networks to optimize the overall networkperformance.

FIG. 5 illustrates an embodiment of how measurement data is locallycollected by a managed-AP.

FIG. 6 illustrates an embodiment of how the measurement data and theadditional AP information collected by a plurality of managed-APs arefurther sent to Wi-Fi network manager 302.

FIG. 7 illustrates an embodiment of how channel allocation adjustmentsare transmitted to the APs by Wi-Fi network manager 302.

FIG. 8 illustrates an embodiment of a graph with six vertices and sevenedges.

FIG. 9 illustrates that by reducing the degree of a particular APthrough appropriate channel allocation, fewer devices from other BSSsshare an edge with the AP/BSS, thereby increasing the number oftransmission opportunities for the BSS.

FIG. 10 illustrates an embodiment of a process 1000 for determiningoptimized adjustments to Wi-Fi parameters associated with one or more ofthe APs.

FIG. 11 illustrates an embodiment of hidden node detection.

FIG. 12 illustrates an embodiment of a process 1200 for detecting ahidden node.

FIG. 13 illustrates an embodiment of a process 1300 for dynamicallyadjusting the Wi-Fi parameters of the APs using conflict graphs.

FIG. 14 illustrates an embodiment of a process 1400 for dynamicallyadjusting the Wi-Fi parameters of the APs using conflict graphs.

FIG. 15 illustrates one embodiment of mixed integer linear programmingthat can be used to minimize the total sum of the weighted degrees ofthe conflict graph by dynamically adjusting the channel allocation ofthe APs.

FIG. 16 illustrates an embodiment of a system for joint optimization ofpower control, channel allocation, and CST control using conflictgraphs.

FIG. 17 illustrates that APs may be divided into a plurality of localneighborhoods for local optimization.

FIG. 18 illustrates one embodiment of a plurality of zones for localoptimization.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Wi-Fi networks are formed using one or more Wi-Fi APs, which can bedeployed in homes, apartments, office buildings, and as outdoorhotspots. FIG. 1 illustrates a Wi-Fi network, in which a Wi-Fi-enabledclient, client 101, client 102, or client 103, may connect to anyauthorized AP (104 or 106) when the client is within the AP's coveragearea. Once a client is within the coverage area of a particular AP, thesignal received by the client generally has a signal strength above thelevel required for connectivity to that AP. However, clients close tothe AP typically receive stronger AP signals than clients farther awayfrom the AP, and enjoy superior performance commensurate with thegreater signal strength. APs are connected to the backbone Internet 108,with traffic routed to and from their clients via standard Internetprotocols. When a client is within the coverage areas of multiple APs,the AP for the Wi-Fi connection is typically selected based on staticclient preferences (e.g., a preferred service set ID or SSID) or signalstrength. For example, client 102 in FIG. 1 is within the coverage areasof two APs (AP 104 and AP 106), but client 102 may connect to AP 104based on its preferred SSID. Wi-Fi APs operate in both the 2.4 GHz and 5GHz spectral bands, with channel bandwidths of 20 MHz, 40 MHz, 80 MHz or160 MHz, depending on the particular Wi-Fi standard used.

One way to reduce interference experienced by the clients in a Wi-Finetwork is via channel allocation. FIG. 1 illustrates that clientsconnected to a particular AP may experience interference caused by otherAPs within the Wi-Fi network. The wireless channels in a Wi-Fi networkare shared across all APs and their clients. When a given channel isused simultaneously by different APs with overlapping coverage areas,the APs create interference to each other's clients, as shown in FIG. 1.The amount of interference depends on many factors, including thepropagation conditions, the carrier frequency, and the distance betweenthe interfering AP and the client. The channel allocation mechanism in802.11 attempts to avoid such interference by assigning differentchannels to APs within close proximity to each other; this channelassignment may be done manually or based on local measurements. Withthis orthogonal frequency reuse, the APs use different frequencies, andinter-AP interference is minimized. While this channel allocation workswell for avoiding interference in low-density AP deployments, in densedeployments there are not enough channels for this method to be viable.For example, only three non-overlapping channels are available in the2.4 GHz band, making it difficult, and at times impossible, for APs withoverlapping coverage areas operating in this band to each be assigned aunique channel.

To mitigate co-channel interference between APs relatively close to eachother that are operating on the same channel, a second interferencemitigation mechanism—carrier sense multiple access with collisionavoidance (CSMA/CA)—is used. CSMA/CA avoids simultaneous transmissionsby two APs on the same channel by staggering the two APs' transmissionsin time. Prior to transmitting a frame, an AP monitors the wirelesschannel for other Wi-Fi transmissions with received power exceeding agiven carrier sense threshold (CST). The CST defines a carrier senseradius around each receiving device: if all transmitters transmit at thesame power, then a transmission from any transmitter within the circlewill lead to the medium being detected as busy at the receiving device.The carrier sense radius as a function of azimuthal angle traces out acircle in ideal free-space propagation. In more typical operatingenvironments, the radius as a function of azimuthal angle will have anirregular shape, due to the different shadowing and multipathexperienced at different client locations. A random back-off timer basedmechanism ensures that listening devices that detect the channel asoccupied will not all simultaneously attempt to transmit as soon as thechannel becomes unoccupied.

Carrier sensing generally works well in low density deployments of APs,where there are sufficient channels to avoid interference between APs,and hence aggressive frequency reuse is not needed. However, in densedeployments, with neighboring APs using the same channel, carriersensing induces a tradeoff between aggressive frequency reuse and higherlevels of interference, as illustrated in FIGS. 2A and 2B. In FIG. 2A,when the CST is high, the carrier sense radius is small. Hence AP 201and AP 202 will transmit simultaneously, causing interference to theother AP's client. If this interference does not preclude successfulpacket reception, then throughput is high; if the interference issufficiently high so as to cause packet errors, then these packets needto be retransmitted, thereby reducing throughput and increasing delay.In FIG. 2B, when the CST is low, the carrier sense radius is large. Thiswill preclude AP 201 and AP 202 from transmitting simultaneously. Ifboth APs have data to transmit, one will back off, thereby reducing itsthroughput and increasing delay.

Current Wi-Fi networks use a static CST that can be AP-specific.Selecting static CST rarely optimizes network performance, since whetheror not two neighboring APs A and B transmit simultaneously depends onwhether the power received from AP A at AP B (and vice versa) is belowthreshold. This is not the best criterion to use: in particular, packeterrors are caused not by the interference between neighboring APs, butby the interference between a given AP and the client associated withthe neighboring AP. As shown in FIG. 2B, with a low CST, AP 201 and AP202 fall within each other's carrier sense radius and hence will nevertransmit simultaneously. However, since client 221 is far from AP 202,and client 223 is far from AP 201, a transmission from AP 201 to client221 could occur simultaneously with a transmission from AP 202 to client223 with minimal interference between them. Fundamentally, whether AP202 should transmit when AP 201 is transmitting should depend on howthat interference impacts the signal received by AP 201's client, i.e.it should depend on the signal-to-interference-plus-noise (SINR) at AP201's client. In addition to adapting to SINR, CSTs should also adapt tothe AP density, as more APs are added to the network, as well as adaptto the client data requirements and changing propagation conditions ofAPs sharing the same channel. Setting the threshold adaptively can bothincrease throughput and reduce packet error probability.

In addition to channel allocation and CST settings, other Wi-Fiparameters, e.g., transmit power control parameters, will also affect aWi-Fi network's overall network performance. For example, reducingtransmit power at a given AP reduces interference between the given APand other devices on the same channel. However, this also reduces SNR(signal-to-noise ratio) and RSSI (receive signal strength indication)between the AP and its connected devices. Thus, transmit power controlmechanisms optimize this tradeoff.

FIG. 3 illustrates an embodiment of a Wi-Fi-network manager forautomatically and dynamically configuring and updating Wi-Fi APparameters in one or more Wi-Fi networks to optimize the overall networkperformance of the one or more Wi-Fi networks. In some embodiments,Wi-Fi network manager 302 connects to the APs and provides service tothe APs as a server. In some embodiments, Wi-Fi network manager 302manages tens of thousands (or more) of APs within the one or more Wi-Finetworks.

FIG. 3 illustrates one exemplary embodiment in which the APs areconnected to Wi-Fi network manager 302 and the Internet 318 using cablemodems. Note that FIG. 3 is provided as an illustrative example only.Therefore, the present application is not limited to this specificexample. For example, the APs managed by Wi-Fi network manager 302 maybe connected to Wi-Fi network manager 302 using network technologiesother than cable modem technologies, including digital subscriber line(DSL) technologies and other Internet technologies.

As shown in FIG. 3, some of the APs managed by Wi-Fi network manager 302may be standalone Wi-Fi APs 304. Alternatively, some of the Wi-Fi APsmay be embedded into other devices, such as cable modems, DSL modems,and the like. Standalone Wi-Fi APs 304 may be connected to a cable modem306, and cable modem 306 may be connected to a cable modem terminationsystem (CMTS) 308 via a hybrid fiber-coaxial (HFC) network 310. CMTS 308may be further connected to Wi-Fi network manager 302 via an operatorcore network 314. Data packets that are sent from CMTS 308 to theInternet 318 are routed through an Internet Gateway 316. Some APs (312)managed by Wi-Fi network manager 302 may be integrated units, eachintegrating a cable modem and a Wi-Fi AP as a single unit. Integratedunits 312 may be connected to CMTS 308 via HFC network 310.

In some embodiments, a software agent is installed on the APs. Forexample, a piece of software may be installed on an AP by an end-user ora network administrator. In another example, an application may bedownloaded from a website, and the application acts as an agent betweenthe AP and Wi-Fi network manager 302. Wi-Fi network manager 302 maymanage, configure, and optimize the parameters of an AP using differentinterface protocols, including Simple Network Management Protocol(SNMP), Control And Provisioning of Wireless Access Points (CAPWAP),Technical Report 069/181 (TR-069/TR-181), Command-Line Interface (CLI),Extensible Markup Language (XML), and the like.

FIG. 4 is a flow chart illustrating an embodiment of a process 400 forautomatically and dynamically configuring and updating Wi-Fi APparameters in one or more Wi-Fi networks to optimize the overall networkperformance. In some embodiments, process 400 is a process that runs onWi-Fi network manager 302 in FIG. 3.

At 402, the APs that are installed in the Wi-Fi networks managed byWi-Fi network manager 302 are discovered. Wi-Fi network manager 302 isconfigured to manage a heterogeneous group of APs. The APs may supportdifferent Wi-Fi protocols, including 802.11a, 802.11c, 802.11g, 802.11n,802.11ac, and the like. The APs can be made by different third-partyvendors. Some of the APs may be purchased off-the-shelf. Therefore, theAPs that are managed by Wi-Fi network manager 302 have a wide range ofcapabilities, configurable parameters, and interfaces. Wi-Fi networkmanager 302 discovers the APs within the Wi-Fi networks, including theircapabilities, configurable parameters, interfaces, and the like. Thisinformation may be used by Wi-Fi network manager 302 to more optimallysearch for a set of configurable parameters for any AP managed by Wi-Finetwork manager 302 to achieve improved overall network performance.

At 406, measurement data is received by Wi-Fi network manager 302 fromthe APs that it manages (hereinafter referred to as managed-APs). Themeasurement data is locally collected by the APs and then sent to Wi-Finetwork manager 302. The measurement data collected by an AP may includedata regarding the AP's clients. The measurement data collected by an APmay also include data regarding clients connected to neighboring APsthat may or may not be APs managed by Wi-Fi-network manager 302. Themeasurement data may include receive signal strength indication (RSSI),throughput, packet error rate, and the like. RSSI is a measurement ofthe power present in a received radio signal. In some embodiments, themeasurement data is Wi-Fi standard-based measurement data (e.g., 802.11standard-based measurement data) measured by the APs. In someembodiments, the APs may collect additional measurement data that isoptional or not specified in the 802.11 standards.

FIG. 5 illustrates an embodiment of how measurement data is locallycollected by a managed-AP. AP 502 is an AP managed by Wi-Fi networkmanager 302. AP 502 can hear from some of its neighboring APs. Forexample, as shown in FIG. 5, AP 502 can hear from APs 504, 506, and 508,but cannot hear from AP 510. The APs that AP 502 can hear from may beeither APs that are managed by Wi-Fi network manager 302 or APs that arenot managed by Wi-Fi network manager 302 (hereinafter referred to asnon-managed-APs). AP 502 may collect different types of measurement datacorresponding to the APs that AP 502 can hear from. For example, AP 502may measure the RSSI, noise, and SNR of the packets from those APs itcan hear from under different conditions and using differentconfigurations, e.g., under different channel assignments, and underdifferent transmit power and CST configurations. In certain modes, e grunning in a promiscuous mode, AP 502 can further decode those packetsto obtain different types of information regarding the packets,including MAC addresses, BSSIDs, MAC packet types, and the like. A basicservice set (BSS) is a single AP together with its associated clients. ABSSID uniquely identifies a BSS. In addition to the measurement datadescribed above, a managed-AP may collect additional AP information,including the AP's MAC address, current channel number, current loadinformation, MAC addresses of associated clients, and the like.

Besides other APs, AP 502 can also hear from its own associated clientsand also some of the neighboring clients that are not associated with AP502. AP 502 may similarly collect different types of measurement datacorresponding to the clients that AP 502 can hear from, as describedabove.

FIG. 6 illustrates an embodiment of how the measurement data and theadditional AP information collected by a plurality of managed-APs arefurther sent to Wi-Fi network manager 302. For example, managed-AP 502sends the measurement data and the AP information at t=T₀, managed-AP504 sends the measurement data and the AP information at t=T₁,managed-AP 506 sends the measurement data and the AP information att=T₂, and so on.

Referring back to FIG. 4, at 408, adjustments to Wi-Fi parametersassociated with one or more APs to optimize the Wi-Fi overall networkperformance are searched. As Wi-Fi network manager 302 receivesmeasurement data and AP information from many APs, including APsinstalled in multiple Wi-Fi networks, the measurement data may be usedby Wi-Fi network manager 302 to compute Wi-Fi parameters that canoptimize network performance in a global sense, achieving superiornetwork performance.

Different Wi-Fi parameters may be dynamically adjusted to optimize theWi-Fi overall network performance. As will be described in greaterdetail below, Wi-Fi parameters that can be dynamically and optimallyadjusted may include CSTs, channel allocation (channel bandwidth andchannel assignment), transmit power, multiple-input multiple-output(MIMO) antenna parameters, backoff parameters, and the like. Forexample, instead of determining channel allocation and CST individuallyor locally, they can be optimized simultaneously in a global sense.

With continued reference to FIG. 4, at 410, at least some of theoptimized adjustments to the one or more Wi-Fi parameters aretransmitted to the one or more APs. The received adjustments may be usedby the APs for self-configuration, self-optimization, and self-healing,such that the APs can collectively form a self-organizing network.

The received adjustments may be used to initialize an AP that has beenrecently installed. For example, after an AP is first installed, the APcollects initial measurement data and sends the data to Wi-Fi networkmanager 302. Wi-Fi network manager 302 then computes the parameters forthe AP and sends them to the AP for self-configuration.

The received adjustments may be used to re-configure an existing AP.When the existing AP connects to Wi-Fi network manager 302 for the firsttime, the existing AP is treated as a new installation for the purposeof network optimization. Wi-Fi network manager 302 computes newparameters for the existing AP based on the received measurement datafrom the existing AP and other APs, and Wi-Fi network manager 302 sendsthe new parameters to the existing AP for reconfiguration.

The received adjustments may also be used to periodically update theWi-Fi parameters of an existing AP. These adjustments are computed basedon dynamic, real-time measurements made periodically by the APs.

The received adjustments may also be used by the APs for self-healingany network topology changes. For example, a network topology change maybe caused by the failure of an AP. Wi-Fi network manager 302 detects thefailure, and the parameters of the surrounding APs are automaticallyadjusted to fill in the resulting coverage hole. In another example, anetwork topology change may be caused by new APs being installed on aWi-Fi network. The network topology change may be detected by Wi-Finetwork manager 302, which is triggered by the detection to initiate anew search.

FIG. 7 illustrates an embodiment of how channel allocation adjustmentsare transmitted to the APs by Wi-Fi network manager 302. For example,the channel allocation adjustments for managed-AP 502 are sent at t=T₀,the channel allocation adjustments for managed-AP 504 are sent at t=T₁,the channel allocation adjustments for managed-AP 506 are sent at t=T₂,and so on. After an AP receives the channel allocation adjustments, theAP can broadcast any new channel assignment to its associated clientsvia a channel switch announcement.

Adjustments to Wi-Fi parameters associated with one or more APs tooptimize the Wi-Fi overall network performance can be determined byWi-Fi network manager 302 based on conflict graphs representing theamount of conflicts between APs.

A graph is a representation of a set of objects where some pairs ofobjects are connected by links. The interconnected objects arerepresented by vertices, and the links that connect some pairs ofvertices are called edges. The degree of a vertex is the number of edgesthat connect to the vertex. FIG. 8 illustrates an embodiment of a graphwith six vertices and seven edges. In this example, the vertices withthe highest degree are vertices 2, 4, and 5, each with a degree of 3.

In a conflict graph representing the amount of conflict between APs,each of the AP/BSS is represented by a vertex. An edge connects two APsif the BSSs of the two APs have an impact on the transmissions andperformance of the other. By reducing the degree of a particular APthrough Wi-Fi parameters adjustments, fewer devices from other BSSswould cause an impact on the AP and its BSS, thereby increasing thethroughput and performance of the AP and its BSS. For example, as shownin FIG. 9, by reducing the degree of a particular AP through appropriatechannel bandwidth and channel assignment, fewer devices from other BSSsshare an edge with the AP/BSS, thereby increasing the number oftransmission opportunities for the BSS. Since the amount of impactcaused by different BSSs on a particular AP/BSS is not the same, theedges of the conflict graph are weighted based on a number of factors,as will be described in greater detail below. The Wi-Fi parameters areadjusted to minimize the total sum of the weighted degrees of theconflict graph using optimization techniques.

FIG. 10 illustrates an embodiment of a process 1000 for determiningoptimized adjustments to Wi-Fi parameters associated with one or more ofthe APs. In some embodiments, process 1000 is a process performing step408 of FIG. 4.

At 1002, the vertices and the edges of the conflict graph aredetermined. Suppose there are n APs/BSSs in the network. Each of theAP/BSS is represented by a vertex in the conflict graph. With n APs,there are a total of n² relationships between each pair of APs. For eachpair of APs, it is determined whether the BSSs of the two respective APshave an impact on the transmissions and performance of one other.Different criteria may be used for determining whether the BSSs have animpact on each other. In some embodiments, it is determined based onwhether the BSSs can hear each other. In some embodiments, it isdetermined based on whether AP1 can decode packets from AP2 or whetherAP2 can decode packets from AP1. In some embodiments, it is determinedbased on whether AP1 can decode packets from an client associated withAP2, or vice versa.

At 1004, weights are assigned to the edges of the conflict graph. Theweights are determined based on a number of factors. In someembodiments, the factors are different types of conflicts between theAPs/BSSs.

One type of conflict is the contention for resources. The amount ofcontention may be determined based on how many devices in BSS1 can hearfrom devices in BSS2: the greater the number of devices that can hearfrom each other, the greater the contention between the devices for theresources and medium. The amount of contention may also be determinedbased on whether or how often the devices in BSS1 need to back off fromtransmission because of transmissions of other devices in BSS2.

Another type of conflict is SINR conflict. SINR issignal-to-interference-plus-noise ratio, which is calculated asSINR=P/(I+N), where P is signal power, I is interference power, and N isnoise power. SINR can be used to measure the quality of wirelessconnections. Devices in one BSS may cause the devices in another BSS tohave lower SINR. For example, if a device in BSS1 does not back off fromtransmission when a device in BSS2 is transmitting, then the device inBSS1 may cause higher interference to the transmission in BSS2. Thehigher the amount of interference, the lower is the SINR, and the lowerare the throughput and performance. Thus, a higher weight may beassigned to an edge connecting two BSSs when one or more of the BSSscauses lower SINR to the other. Conversely, a lower weight may beassigned to an edge connecting two BSSs when neither of the BSSs causesdegradation of SINR to each other beyond a predetermined threshold.

One type of conflict is conflict caused by hidden nodes. FIG. 11illustrates an embodiment of hidden node detection. In FIG. 11, client1106 is associated with AP 1101, while client 1108 is associated with AP1102. AP 1101 cannot hear and decode packets from AP1102, but client1106 and AP 1102 can hear each other. Suppose AP 1102 begins to transmitfirst, and client 1106 can hear and decode the packets from AP 1102.Since AP 1101 cannot hear and decode packets from AP 1102, AP 1101 doesnot back off from transmission and begins to transmit to client 1106 aswell. With both AP 1101 and AP 1102 transmitting at the same time andcreating interference to each other, client 1106 will likely drop thepackets from AP 1101. Here, AP 1102 is a hidden node when AP 1101transmits to client 1106. A higher weight may be assigned to an edgeconnecting two APs when one or more of the APs is a hidden node to theother. Conversely, a lower weight may be assigned to an edge connectingtwo APs when neither of the APs is a hidden node to the other.

Hidden nodes may be detected by Wi-Fi network manager 302 because Wi-Finetwork manager 302 can collect information regarding the APs (bothmanaged-APs and non-managed-APs), their associated clients, and whetherthe APs and their clients can hear and decode packets from each other.For example, this information may be collected by Wi-Fi network manager302 at step 406 of process 400 as described above. This information isnot available when the APs are running in a distributed mode, withoutany centralized network manager to collect measurement data and otherinformation from the APs, monitor their conditions, and manage theirconfigurations.

FIG. 12 illustrates an embodiment of a process 1200 for detecting ahidden node. At 1202, it is determined whether AP 1101 can hear anddecode packets from AP 1102. The determination may be based on whetherAP 1101 has sent any measurement data to Wi-Fi network manager 302regarding packets being received from AP 1102 or not. Wi-Fi networkmanager 302 can distinguish a non-managed AP from a client based onwhether beacon frames are decoded by the managed-APs or not. Beaconframes are transmitted by the AP in a BSS, but are not transmitted bythe clients in the BSS.

At 1204, if AP 1101 cannot hear from AP 1102, then it is determinedwhether AP 1102 can hear from any clients of AP 1101. At 1206, if AP1102 can hear from one or more clients of AP 1101, then Wi-Fi networkmanager 302 may deduce that one or more clients of AP 1101 can also hearfrom AP 1102 and further that a partial hearing of AP 1102 is detectedin the BSS of AP 1101. In some embodiments, a hidden node is detectedbased on the detected partial hearing of AP 1102.

At 1208, additional criteria may be used to determine whether a hiddennode is detected. In some embodiments, a hidden node is detected basedfurther on whether the clients of AP 1101 can tolerate the interferencefrom AP 1102 and whether the clients of AP 1102 can tolerate theinterference from AP 1101. In some embodiments, a hidden node isdetected based further on whether the traffic is mainly upstream (i.e.,from clients to AP), mainly downstream (i.e., from AP to clients), orboth.

In some embodiments, packet success/failure rates may be used toestimate whether a hidden node problem exists on a given channel beforeprocess 1200 is performed. For example, prior to process 1200, packetsuccess and failure rates of the APs and clients may be monitored. Ifthe packet failure rate is above a predetermined threshold, then process1200 is used to further determine whether a hidden node exists. Theadditional step of monitoring the packet success/failure rates is usefulin reducing the computation required for analyzing the packetinformation received from AP 1201 and AP 1202.

Another factor that can be used to determine the weight of an edge ofthe conflict graph is the load associated with an AP/BSS. When an AP ishandling a greater amount of wireless traffic or load in the network,the AP experiences greater performance degradation in the presence ofany types of the conflicts described above than an AP that is handling alighter load. For example, when an AP is handling a relatively lightload, the performance degradation caused by hidden nodes may benegligible. In another example, when an AP is handling a relativelylight load, the level of contention may be insignificant even when theAP can hear from many devices from other BSSs. Therefore, a higherweight may be assigned to an edge connecting two BSSs when one or moreof the BSSs have higher network loads. Conversely, a lower weight may beassigned to an edge connecting two BSSs when neither of the BSSs hasheavy network loads. The load associated with an AP/BSS may bedetermined based on the number of clients associated with the AP, thenumber of packets, the duration of the packets sent in the BSS, and thelike.

Another factor that can be used to determine the weight of an edge ofthe conflict graph is the backoff behavior of the devices within thenetwork. For example, weight can be assigned based on which devices backoff to whom, the number of such backoffs, the number of asymmetricbackoffs where device A backs off to device B, but not vice versa.

An illustrative example of assigning weights to the edges of theconflict graph is given below:

-   -   1. If at least one device in first BSS backs off when another        device in the second BSS transmits, or vice versa, then the        conflict between the two BSS is assigned a weight of at least        one.    -   2. If there is a hidden node with partial hearing of one of the        following types, then the conflict between the two BSSs is        assigned a weight of at least w₁:        -   a. AP1 does not hear AP2 but client of AP1 hears AP2;        -   b. AP2 does not hear AP1 but client of AP2 hears AP1;        -   c. client of AP1 does not hear AP2 but AP1 hears AP2;        -   d. client of AP2 does not hear AP1 but AP2 hears AP1.    -   3. If a device in one BSS does not back off to a device in the        other BSS, but simultaneous transmissions by the two devices        lead to a SINR less than a threshold in the second BSS, then        assign the conflict a weight of W₂.

With continued reference to FIG. 10, at 1006, optimized adjustments toWi-Fi parameters associated with the APs are determined based on theconflict graphs. The Wi-Fi parameters are adjusted to minimize the totalsum of the weighted degrees across all the APs using optimizationtechniques.

Using dynamic channel allocation as an illustrative example, if thereare M possible channels, M channel-specific conflict graphs as afunction of channel allocation are obtained. Optimization techniques areused to minimize the sum of the weighted degrees across all the APs. Forexample, if an AP has 3 weighted edges, x, y, and z, then the weighteddegrees of AP is x+y+z. The sum of the weighted degrees across all theAPs is then minimized. The weighted edges of an AP are determined forthe channel the AP is allocated to. When the APs can support differentchannel bandwidths, the dynamic channel allocation is performed for allpossible combinations of channel bandwidths and channel assignmentsassociated with the different APs. The choice of channel bandwidthassigned to each AP is then based on minimizing the sum of the weighteddegrees across all the APs, where the weights will depend on the channelbandwidth along with other factors.

In some embodiments, the optimization further minimizes the maximumweighted degree of any AP residing on a given channel. For example, iftwo different channel allocations both result in the same total sum ofthe weighted degrees across all the APs, but the maximum weighted degreeof any AP resulting from the first channel allocation is smaller thanthe second channel allocation, then the first channel allocation isselected. The advantage of minimizing the maximum weighted degree isthat the APs will have more even and similar performance, instead ofhaving some APs with superior performance and some APs withsignificantly worse performance. In some cases, relaxing the maximumweighted degree constraint may further minimize the sum of the weighteddegrees across all the APs, at the expense of worsening the performanceof some of the APs.

FIG. 13 illustrates an embodiment of a process 1300 for dynamicallyadjusting the Wi-Fi parameters of the APs using conflict graphs. In someembodiments, process 1300 is performed during step 1106 of FIG. 10. At1302, the upper and lower bounds of a feasible maximum weighted degreeare computed. At 1304, Wi-Fi parameters are adjusted such that the sumof the weighted degrees across all the APs is minimized subject to themaximum degree constraint. At 1306, it is determined whether a feasiblesolution exists if the maximum weighted degree range is reduced. If afeasible solution is available, then the maximum weighted degree rangeis reduced and step 1304 is repeated again. Otherwise, process 1300 isterminated.

FIG. 14 illustrates an embodiment of a process 1400 for dynamicallyadjusting the Wi-Fi parameters of the APs using conflict graphs. In someembodiments, process 1400 is performed during step 1106 of FIG. 10. At1402, the upper and lower bounds of a feasible maximum weighted degreeare computed. At 1404, a maximum weighted degree candidate isinitialized to a value within the upper and lower bounds. At 1406, anapproximation solution to the optimization problem is computed using agreedy heuristic. The approximation solution is obtained subject to themaximum weighted degree constraint. A greedy algorithm is an algorithmthat follows the problem solving heuristic of making the locally optimalchoice at each stage with the hope of finding a global optimum. In manyproblems, a greedy strategy does not in general produce an optimalsolution, but nonetheless a greedy heuristic may yield locally optimalsolutions that approximate a global optimal solution in a reasonabletime. If the approximate solution obtained at step 1406 indicates thatthe current maximum degree candidate will likely yield a set of Wi-Fiparameters that are close to the optimum, then the maximum degree isdetermined, and a full-blown optimization is performed subject to themaximum degree constraint at 1412. Otherwise, at 1410, the maximumdegree candidate is adjusted to ensure that imposing this constraintdoes not increase the cost function by more than a fixed percentage. Anapproximation solution to the optimization problem is again computedusing the greedy heuristic at 1406. Steps 1406, 1408, and 1410 arerepeated until a maximum degree is determined. With the determinedmaximum degree, a full-blown optimization is performed subject to themaximum degree constraint at 1412.

The full-blown optimization performed at 1412 may be any optimizationtechnique. For example, mixed integer linear programming techniques maybe used. Linear programming is a mathematical method for determining away to achieve the best outcome in a given mathematical model for somelist of requirements represented as linear relationships. FIG. 15illustrates one embodiment of mixed integer linear programming that canbe used to minimize the total sum of the weighted degrees of theconflict graph by dynamically adjusting the channel allocation of theAPs.

Different Wi-Fi parameters may be dynamically adjusted using theaforementioned techniques to optimize the Wi-Fi overall networkperformance. In addition to channel allocation, the aforementionedtechniques may be used for transmit power control, CST adjustment, andso on. In some embodiments, a few types of Wi-Fi parameters may beadjusted jointly, e.g., channel allocation may be performed jointly withpower control, and power control jointly with CST.

FIG. 16 illustrates an embodiment of a system for joint optimization ofpower control, channel allocation, and CST control using conflictgraphs. Conflict graph creation block 1602 creates conflict graphs and alist of conflicts based on various inputs, including inputs from thepower optimization block 1604 and the CST optimization block 1606. Theconflict graphs and the list of conflicts are then fed into the channeloptimization block 1608 for channel allocation. After channeloptimization, the channel allocation output is further fed back into thesystem again for further CST and power optimization.

An illustrative example of dynamically adjusting the transmit power ofthe APs is given below:

-   -   1. Set power for APX based on a common parameter p for all APs        in the network. For example:        -   a. all the APs are set to the same power; or        -   b. an AP's power can be determined as p+a PL, where 0<a<1 is            a constant and PL is the path loss of the client furthest            away from the AP.    -   2. Compute the following statistics for the conflict graph for        different values of p (note that the set of conflicts and        weights change as a function of the power levels):        -   a. Total number of conflicts;        -   b. Number of hidden node conflicts;        -   c. Number of SINR conflicts;        -   d. Number of device pairs where one device backs off to a            second one, but not vice versa.    -   3. Select a value of p which minimizes a cost function of the        above parameters and/or imposes a bound on each of the        quantities above.

An illustrative example of joint channel and power optimization of theAPs is given below:

-   -   1. Set the power for APX based on a common parameter p for all        APs in the network.    -   2. For different values of p, compute the conflict graph and use        a greedy channel allocation heuristic to compute the following        statistics for the resulting channel allocation:        -   a. Total number of conflicts;        -   b. Number of hidden node conflicts;        -   c. Number of SINR conflicts;        -   d. Number of device pairs where one device backs off to a            second one, but not vice versa.    -   3. Select a value of p which minimizes a cost function of the        above parameters and/or imposes a bound on each of the        quantities above.

An illustrative example of dynamically adjusting the CST of the APs isgiven below:

-   -   1. Set CST for APX based on a common parameter p for all APs in        the network. For example,        -   a. all the APs are set to the same CST        -   b. an AP's CST can be determined as AP_TX_power-PL-p, where            PL is the path loss of the client furthest away from the AP,            and AP_TX_power is the transmission power of the AP    -   2. Compute the following statistics for the conflict graph for        different values of p:        -   a. Total number of conflicts;        -   b. Number of hidden node conflicts;        -   c. Number of SINR conflicts;        -   d. Number of device pairs where one device backs off to a            second one, but not vice versa.    -   3. Select a value of p which minimizes a cost function of the        above parameters and/or imposes a bound on each of the        quantities above.

In some embodiments, in addition to global optimization, localneighborhoods may be created for local optimization. FIG. 17 illustratesthat APs may be divided into a plurality of local neighborhoods forlocal optimization. As shown in FIG. 17, APs 1702 within a localizedregion 1706 are grouped together for local optimization. Similarly, APs1704 within another localized region 1708 are grouped together for localoptimization. A local optimization may be performed prior to a globaloptimization across the entire network. The local optimization has alower computational complexity, and it avoids changing parameters acrossthe entire network at the same time. Conversely, the global optimizationavoids local minima that may arise when only local parameters within aneighborhood are accounted for in the local optimization.

Local optimization may be triggered by network topology or networkcondition changes to re-optimize the Wi-Fi parameters of a localizedregion. For example, an AP in a localized region may fail or a new APmay be installed in the localized region. Local optimization may also betriggered periodically by a timer to re-optimize the Wi-Fi parameters ofa localized region.

Global optimization may be triggered by more significant networktopology or network condition changes to re-optimize the Wi-Fiparameters of the entire network. It can also be triggered periodicallybut less frequently than local optimizations. In general, a globaloptimization is triggered when it is determined that the re-optimizationwill likely result in significant improvement in performance. Theselection between global optimization versus local optimization is basedon different factors, including the number of Wi-Fi parameter changes,computational complexity, performance gain, and the like.

In some embodiments, a local optimization may be initiated by Wi-Finetwork manager 302. In some embodiments, a local optimization may beinitiated and performed by an AP that is selected as a local controllerwithin a localized region. In some embodiments, a local optimization maybe initiated and performed by a new AP. In a local optimization, only aportion of the conflict graph corresponding to the entire network isupdated. The update is based on measurement data received from a portionof the APs.

In some embodiments, a local optimization re-optimizes the Wi-Fiparameters of the APs within a first zone, and takes into account theperformance of some of the APs in the network within a second zone, butdoes not take into account the performance of the APs beyond the secondzone. FIG. 18 illustrates one embodiment of a plurality of zones forlocal optimization. During a local optimization, the performance of theAPs in the first zone 1804 and the second zone 1806 are taken intoaccount, but only the Wi-Fi parameters of APs in the first zone 1804 arere-optimized and re-configured. In some embodiments, the division of thezones may be based on a distance from a center point of the localoptimization, or based on the number of hop counts away from a centerpoint of the local optimization. For example, the center point of thelocal optimization may be a new AP or a failed AP. FIG. 18 illustratesthat the first zone 1804 is defined as APs that are within two hops fromthe center point 1802, and the second zone 1806 is defined as APs thatare 3 hops away, and the third zone 1808 is defined as APs that are morethan 3 hops away. In this example, the portion of the conflict graphthat is updated may include APs/BSSs corresponding to zone 1806. In someembodiments, the division of the zones is based on a heuristic. Forexample, the heuristic may determine the first zone to include the APshaving performance below a predetermined performance target level. Inanother example, the heuristic may determine the first zone to includethe APs with the most performance degradation within a time interval.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method of dynamically adjusting Wi-Fiparameters of a plurality of access points (APs), comprising: receivingreports from the plurality of APs through an interface; creating aconflict graph by a processor, comprising: creating a plurality ofvertices of the conflict graph, each vertex corresponding to one of theplurality of APs; determining that there is a conflict between at leastsome of the pairs of APs based at least in part on the received reports;and connecting an edge between each of the at least some of the pairs ofAPs; assigning a weight to each of the edges of the conflict graph basedon a plurality of factors; and adjusting a plurality of Wi-Fi parametersof one or more of the plurality of APs to minimize a total of allweighted degrees using an optimization technique, wherein the pluralityof Wi-Fi parameters comprises a plurality of transmit power parameters,channel allocation parameters, carrier sense threshold (CST) parameters,multiple-input multiple-output (MIMO) antenna parameters, backoffparameters, client load, and channel bandwidth.
 2. The method of claim1, wherein the weight is assigned based at least in part on a degree ofcontention for resources between a pair of APs.
 3. The method of claim1, wherein the weight is assigned based at least in part on adegradation of signal-to-interference-plus-noise ratio (SINK) caused bya first AP to a second AP.
 4. The method of claim 1, wherein the weightis assigned based at least in part on whether a first AP is a hiddennode with respect to a second AP.
 5. The method of claim 1, wherein theweight is assigned based at least in part on a load associated with anAP.
 6. The method of claim 1, wherein the weight is assigned based onbackoff behaviors between APs and their clients.
 7. The method of claim1, wherein the weight is assigned based on the channel bandwidthassigned to an AP.
 8. The method of claim 1, wherein the plurality ofWi-Fi parameters are jointly optimized.
 9. The method of claim 1,wherein the plurality of Wi-Fi parameters comprises CST parameters, andwherein the adjustments to the CST parameters are further based on oneor more of the following: SINK, AP density, client data requirements,and propagation conditions of APs sharing the same channel.
 10. Themethod of claim 1, wherein the search is subject to a maximum weighteddegree constraint, wherein the maximum weighted degree constraint limitsthe weighted degree of each AP to a predetermined threshold, and whereinthe maximum weighted degree constraint is determined based at least inpart on a greedy heuristic.
 11. The method of claim 1, wherein a reportreceived from an AP comprises measurement data collected by the AP, themeasurement data comprising one or more of the following: receive signalstrength indication (RSSI) of packets sent by another AP or anotherclient, noise level of packets sent by another AP or another client, andsignal-to-noise ratio (SNA) of packets sent by another AP or anotherclient.
 12. The method of claim 1, wherein a report received from an APcomprises information corresponding to the AP, the informationcomprising one or more of the following: a MAC address of the AP, acurrent channel assigned to the AP, load information of the AP, and MACaddresses of clients associated with the AP.
 13. The method of claim 1,wherein the adjusting comprises reducing a degree of a particular APthrough the Wi-Fi parameters, such that fewer devices from other BSSscause an impact on the AP and its BSS, thereby increasing the throughputand performance of the AP and its BSS.
 14. The method of claim 1,wherein the creating is performed responsive to detecting a topologychange.
 15. The method of claim 1, wherein assigning the weight is basedon a plurality of parameters that impact transmission with eachparameter's weight selected based on associated impact for each of theplurality of factors.
 16. A system for dynamically adjusting Wi-Fiparameters of a plurality of access points (APs), comprising: aninterface configured to receive reports from the plurality of APs; aprocessor configured to: create a conflict graph, comprising: creating aplurality of vertices of the conflict graph, each vertex correspondingto one of the plurality of APs; determining that there is a conflictbetween at least some of the pairs of APs based at least in part on thereceived reports; and connecting an edge between each of the at leastsome of the pairs of APs; assigning a weight to each of the edges of theconflict graph based on a plurality of factors; adjusting a plurality ofWi-Fi parameters of one or more of the plurality of APs to minimize atotal of all weighted degrees using an optimization technique, whereinthe plurality of Wi-Fi parameters comprises a plurality of transmitpower parameters, channel allocation parameters, carrier sense threshold(CST) parameters, multiple-input multiple-output (MIMO) antennaparameters, backoff parameters, client load, and channel bandwidth; anda memory coupled to the processor and configured to provide theprocessor with instructions.
 17. The system of claim 16, wherein theweight is assigned based at least in part on a degree of contention forresources between a pair of APs.
 18. The system of claim 16, wherein theweight is assigned based at least in part on a degradation ofsignal-to-interference-plus-noise ratio (SINK) caused by a first AP to asecond AP.
 19. The system of claim 16, wherein the weight is assignedbased at least in part on whether a first AP is a hidden node withrespect to a second AP.
 20. The system of claim 16, wherein the weightis assigned based at least in part on a load associated with an AP. 21.The system of claim 16, wherein the weight is assigned based on backoffbehaviors between APs and their clients.
 22. The system of claim 16,wherein the weight is assigned based on the channel bandwidth assignedto an AP.
 23. The system of claim 16, wherein the plurality of Wi-Fiparameters are jointly optimized.
 24. The system of claim 16, whereinthe plurality of Wi-Fi parameters comprises CST parameters, and whereinthe adjustments to the CST parameters are further based on one or moreof the following: SINR, AP density, client data requirements, andpropagation conditions of APs sharing the same channel.
 25. The systemof claim 16, wherein the search is subject to a maximum weighted degreeconstraint, wherein the maximum weighted degree constraint limits theweighted degree of each AP to a predetermined threshold, and wherein themaximum weighted degree constraint is determined based at least in parton a greedy heuristic.
 26. The system of claim 16, wherein a reportreceived from an AP comprises measurement data collected by the AP, themeasurement data comprising one or more of the following: receive signalstrength indication (RSSI) of packets sent by another AP or anotherclient, noise level of packets sent by another AP or another client, andsignal-to-noise ratio (SNA) of packets sent by another AP or anotherclient.
 27. The system of claim 16, wherein a report received from an APcomprises information corresponding to the AP, the informationcomprising one or more of the following: a MAC address of the AP, acurrent channel assigned to the AP, load information of the AP, and MACaddresses of clients associated with the AP.
 28. The system of claim 16,wherein the adjusting comprises reducing a degree of a particular APthrough the Wi-Fi parameters, such that fewer devices from other BSSscause an impact on the AP and its BSS, thereby increasing the throughputand performance of the AP and its BSS.
 29. The system of claim 16,wherein the creating is performed responsive to detecting a topologychange.
 30. The system of claim 16, wherein assigning the weight isbased on a plurality of parameters that impact transmission with eachparameter's weight selected based on associated impact for each of theplurality of factors.
 31. A computer program product for dynamicallyadjusting Wi-Fi parameters of a plurality of access points (APs), thecomputer program product being embodied in a non-transitory computerreadable storage medium and comprising computer instructions for:receiving reports from the plurality of APs; creating a conflict graph,comprising: creating a plurality of vertices of the conflict graph, eachvertex corresponding to one of the plurality of APs; determining thatthere is a conflict between at least some of the pairs of APs based atleast in part on the received reports; and connecting an edge betweeneach of the at least some of the pairs of APs; assigning a weight toeach of the edges of the conflict graph based on a plurality of factors;and adjusting a plurality of Wi-Fi parameters of one or more of theplurality of APs to minimize a total of all weighted degrees using anoptimization technique, wherein the plurality of Wi-Fi parameterscomprises a plurality of transmit power parameters, channel allocationparameters, carrier sense threshold (CST) parameters, multiple-inputmultiple-output (MIMO) antenna parameters, backoff parameters, clientload, and channel bandwidth.
 32. The computer program product of claim31, wherein the adjusting comprises reducing a degree of a particular APthrough the Wi-Fi parameters, such that fewer devices from other BSSscause an impact on the AP and its BSS, thereby increasing the throughputand performance of the AP and its BSS.
 33. The computer program productof claim 31, wherein the creating is performed responsive to detecting atopology change.
 34. The computer program product of claim 31, whereinassigning the weight is based on a plurality of parameters that impacttransmission with each parameter's weight selected based on associatedimpact for each of the plurality of factors.